data lakehouse explained

The concept of the data lakehouse has gained significant attention in recent years as a new approach to managing and analyzing data. In this article, we will delve into what a data lakehouse is, its key components, and its benefits for organizations.

What is a Data Lakehouse?

A data lakehouse is a hybrid data architecture that combines the best features of a data warehouse and a data lake. It aims to address the limitations and challenges associated with traditional data warehousing and data lakes, providing organizations with a more flexible and scalable solution for managing and analyzing their data.

The Data Warehouse and the Data Lake

To understand the significance of the data lakehouse, let's briefly look at the data warehouse and the data lake. In the early 2000s, data warehouses emerged as the go-to solution for storing and analyzing structured data. These warehouses allowed organizations to separate transactional and analytical workloads and provided a structured environment for data processing.

However, data warehouses came with certain limitations. They required extensive Extract, Transform, Load (ETL) processes to prepare and structure data for analysis, which often led to delays and increased complexity. Additionally, data warehouses were primarily designed for structured data and struggled to handle the vast volumes of unstructured and semi-structured data that started to become prevalent.

This is where data lakes came into play. Data lakes enabled organizations to store raw, unprocessed data in its native format, including structured, unstructured, and semi-structured data. The flexibility and scalability of data lakes made them popular for data storage, exploration, and data science use cases.

However, data lakes also introduced challenges. The lack of structure and schema-on-read approach made it difficult to perform complex analytics and queries directly on the data. Data lakes required additional processing steps, such as data transformation and schema enforcement, before analysis could take place. This created a barrier for business users and introduced complexities in data governance and data quality.

The Data Lakehouse Approach

The data lakehouse architecture aims to bridge the gap between data warehouses and data lakes by combining their strengths while addressing their limitations. It provides a unified platform that allows organizations to store, process, and analyze vast amounts of structured and unstructured data in a flexible and scalable manner.

At its core, a data lakehouse typically consists of three main components:

  1. Object Store: The object store serves as the storage layer for the data lakehouse. It is designed to handle large volumes of data in various formats, such as CSV, JSON, Parquet, or Avro. The object store provides durability, scalability, and cost-effectiveness for storing data in its raw form.
  2. Semantic Layer: The semantic layer adds structure and schema to the data lake, enabling easier data access and analysis. This layer typically leverages relational database concepts, allowing users to interact with the data using SQL queries and relational operations. The semantic layer provides a unified view of the data, abstracting away the underlying file formats and enabling efficient analytics.
  3. Compute Engines: Compute engines are responsible for processing and analyzing the data stored in the object store through the semantic layer. Different compute engines, such as Apache Spark, Presto, Athena, or Apache Impala, can be utilized based on specific use cases and preferences. Compute engines decoupled from the storage layer provide scalability and flexibility, allowing organizations to choose the most suitable engine for their needs.

Benefits of a Data Lakehouse

The data lakehouse architecture offers several advantages that make it an appealing choice for organizations looking to unlock the full potential of their data:

  1. Flexibility: By combining the schema-on-read flexibility of data lakes with the structure of data warehouses, a data lakehouse allows users to work with diverse data types and formats without the need for upfront schema design. This flexibility enables agile data exploration and faster time-to-insight.
  2. Scalability: The scalability of the data lakehouse architecture allows organizations to handle massive volumes of data. The decoupling of compute and storage resources enables independent scaling, ensuring efficient resource utilization and cost-effectiveness.
  3. Cost-efficiency: Object stores used in data lakehouses, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage, offer cost-effective storage options, especially when dealing with large amounts of data. Additionally, the decoupled compute resources enable organizations to scale up or down based on their specific needs, optimizing costs.
  4. Improved Data Governance: The semantic layer in a data lakehouse enhances data governance capabilities. It provides a centralized and structured view of the data, making it easier to enforce data quality, access controls, and data lineage. Organizations can establish data governance policies and apply them consistently across different datasets.
  5. Ecosystem Compatibility: The data lakehouse architecture leverages popular open-source and commercial tools, allowing organizations to work with their existing analytics and data processing frameworks. It provides compatibility with SQL-based tools, data integration platforms, data science libraries, and business intelligence tools, making it easier to integrate into existing workflows and ecosystems.

Real-World Examples

Several companies have adopted the data lakehouse architecture to unlock the value of their data. Here are a few notable examples:

  • Netflix: Netflix engineers created Apache Iceberg, a table format optimized for large-scale analytics on the data lakehouse architecture. Iceberg gained popularity and support from various platforms, enabling Netflix to efficiently manage and analyze their vast streaming data.
  • Uber: Uber created Hudi, an open-source library that handles rapid incremental updates in data lakes. Hoodie enabled Uber to process real-time data updates and efficiently manage its data lakehouse architecture.
  • Databricks: Databricks, the company behind Apache Spark, introduced Delta Lake, an open-source storage layer that adds reliability, ACID transactions (A.C.I.D. stands for atomicity, consistency, isolation, and durability), and schema enforcement to data lakes. Delta Lake simplifies data management and improves data quality in the data lakehouse context.


The data lakehouse represents a new paradigm in data management, combining the strengths of data warehouses and data lakes while mitigating their limitations. By providing flexibility, scalability, and improved data governance, the data lakehouse architecture enables organizations to unlock the full potential of their data assets. With a growing ecosystem of tools and platforms supporting the data lakehouse concept, organizations have more options than ever to leverage their data effectively and drive valuable insights for their business.

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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